Science China Earth Sciences

, Volume 54, Issue 1, pp 10–18 | Cite as

Discriminant Genetic Algorithm Extended (DGAE) model for seasonal sand and dust storm prediction

  • YuanQin Yang
  • JiZhi Wang
  • Qing Hou
  • Yi Li
  • ChunHong Zhou
Research Paper

Abstract

Here we use a Discriminant Genetic Algorithm Extended (DGAE) model to diagnose and predict seasonal sand and dust storm (SDS) activities occurring in Northeast Asia. The study employed the regular meteorological data, including surface data, upper air data, and NCEP reanalysis data, collected from 1980–2006. The regional, seasonal, and annual differences of 3-D atmospheric circulation structures and SDS activities in the context of spatial and temporal distributions were given. Genetic algorithms were introduced with the further extension of promoting SDS seasonal predication from multi-level resolution. Genetic probability was used as a substitute for posterior probability of multi-level discriminants, to show the dual characteristics of crossover inheritance and mutation and to build a non-linear adaptability function in line with extended genetic algorithms. This has unveiled the spatial distribution of the maximum adaptability, allowing the forecast field to be defined by the population with the largest probability, and made discriminant genetic extension possible. In addition, the effort has led to the establishment of a regional model for predicting seasonal SDS activities in East Asia. The model was tested to predict the spring SDS activities occurring in North China from 2007 to 2009. The experimental forecast resulted in highly discriminant intensity ratings and regional distributions of SDS activities, which are a meaningful reference for seasonal SDS predictions in the future.

keywords

sand and dust storms seasonal prediction methodology Discriminant Genetic Algorithm Extended (DGAE) model 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Zhang Q, Gao G, Wang M, et al. Monitoring and early warning services for spatial and temporal changes of droughts and floods In China in recent 50 years (in Chinese). 200 Years Annual Meeting of Chinese Meteorological Society. Beijing: China Meteorological Press, 2004. 383–384Google Scholar
  2. 2.
    Li C Y. Introduction to Dynamic Climate (in Chinese). Beijing: China Meteorological Press, 1995. 1–4Google Scholar
  3. 3.
    Ding J, Zhu T. Heterogeneous reactions on the surface of fine particles in the atmosphere. Chin Sci Bull, 2003, 48: 2267–2276CrossRefGoogle Scholar
  4. 4.
    Shi G Y, Zhao S X. On the scientific problems of sandstorm study (in Chinese). Chin J Atmos Sci, 2003, 27: 591–606Google Scholar
  5. 5.
    Shen S H, Chen S J. Numerical simulation for frontogenesis process due to radiation forcing of sand storms (in Chinese). Acta Meteorol Sin, 2003, 27: 591–606Google Scholar
  6. 6.
    Wang S G, Yang D B, Jin T, et al. Black storm in Northwest China causes and countermeasures (in Chinese). J Desert Res, 1995, 15: 19–30Google Scholar
  7. 7.
    Zhang X Y. Spring Sandstorms in Northeast Asia in 2006 (in Chinese). Beijing: China Meteorological Press, 2006. 9–19Google Scholar
  8. 8.
    Thompson D W J, Wallace J M. The Arctic Oscillation signature in the wintertime geo-potential height and temperature fields. Geophys Res Lett, 1998, 25: 1297–1300CrossRefGoogle Scholar
  9. 9.
    He C, He J H. The relationship between the northern polar oscillation and temperature variations in winter (in Chinese). J Nanjing Instit Meteorol, 2003, 26: 1–7Google Scholar
  10. 10.
    Swap R. Dust in the Amazon Basin. Tellus, 1992, 44B: 133–149Google Scholar
  11. 11.
    Franzen L G. The Saharan dust episode of southern and central Europe, and northern Scandinavia March, 1991. Weather, 1995, 50: 313–318Google Scholar
  12. 12.
    Genthon C. Simulations of desert dust and sea-salt aerosols in Antarctica with a general circulation model of the atmosphere. Tellus Ser B-Chem Phys Meteoral, 1992, 44: 371–389CrossRefGoogle Scholar
  13. 13.
    Joseph P V, Raipal D K, Deka S N, et al. The convective dust storms of Northwest India. Mausam, 1980, 31: 431–442Google Scholar
  14. 14.
    Wolfson N, Marson M. Satellite 1986: Observations of a phantom the desert. Weather, 1986, 41: 57–60Google Scholar
  15. 15.
    Iwasaka Y. The transport and spatial scale of Asian dust-storm clouds: A case study of the dust-storm event of April, 1979. Tellus Ser B-Chem Phys Meteoral, 1983, 35: 189–196CrossRefGoogle Scholar
  16. 16.
    Ellis J R, W G, Merrill J T. Trajectories for Saharan dust transported to Barbados using Stokes’s law to describe gravitational settling. J Applied Meteorol, 1995, 34: 1716–1726CrossRefGoogle Scholar
  17. 17.
    Cuevas E, Baldasano J M, Pérez C, et al. The SDS-GEOEurope GEO-System oriented System. International Sand and Dust Storm Warning System. Barcelona: WMO/GEO Expert Meeting, 2007. 7–9Google Scholar
  18. 18.
    Gao Q X, Su F Q, Ren Z H, et al. Sandstorms in Beijing and its impact (in Chinese). China Envirnment, 2002, 32: 468–471Google Scholar
  19. 19.
    Zhang S L, Gong S L, Shen Z X, et al. Characterization of soil dust aerosol in China and its transport and distribution during 2003 ACE-Asia. J Geophys Res, 2003, 108(D9): 4261, doi: 10.1029/2002JD002632CrossRefGoogle Scholar
  20. 20.
    Ye D Z, Chou J F, Liu J Y. A study on the causes of sandstorms in northern China and its countermeasures (in Chinese). Acta Geograph Sin, 2000, 55: 513–521Google Scholar
  21. 21.
    Kang D J, Wang H J. Analysis on the decadal scale variation of the dust storm in North China. Sci China Ser D-Earth Sci, 2005, 48: 2260–2266CrossRefGoogle Scholar
  22. 22.
    Yang Y Q, Hou Q, Zhou C H, et al. Sand/dust storm processes in Northeast Asia and associated large-scale circulations. Atmos Chem Phys, 2008, 8: 25–33CrossRefGoogle Scholar
  23. 23.
    Wang Y Q, Zhang X Y. The contribution from distant dust spruces to the atmospheric particulate matter loading at Xi’an, China during spring. Sci Total Environment, 2006, 368: 875–883CrossRefGoogle Scholar
  24. 24.
    Wang J Z, Yang Y Q, Zhou C H, et al. A study on weather process characteristics of spring SDS in 1980–2007. In: Fifth International Workshop on Sand and Dust Storms (SDS). Beijing: Meteorology Press, 2008. 9–11Google Scholar
  25. 25.
    Sun J, Li Z C. A Preliminary study on Sandstorms forecast method in the northwest of Chin (in Chinese). Meteorol Monthly, 2001, 27: 19–24Google Scholar
  26. 26.
    Fan K, Wang H J. Interannual variability of Antarctic Oscillation and its influence on East Asian climate during boreal winter and spring. Sci China Ser D-Earth Sci, 2006, 49: 554–560CrossRefGoogle Scholar
  27. 27.
    Gong S L, Barrie L A, Blanchet J P, et al. Canadian aerosol module: A size-segregated simulation of atmospheric aerosol processes for climate and air quality models 1. Module development. J Geophys Res, 108(D1): 4007, doi: 10.1029/20012003JD002002Google Scholar
  28. 28.
    Folland C K, Colman A. A multivariate technique for use in long-range forecasting. Programme Long-range Forecasting WMO/TD, 1985, 87: 628–636Google Scholar
  29. 29.
    Song L C, Yu Y X, Sun X Y, et al. Relationship between Arctic Oscillation and strong sandstorm in the North of China (in Chinese). Plateau Meteorol, 2004, 23: 835–839Google Scholar
  30. 30.
    Li Y, Wang J Z, Chen L S, et al. Study on rainfall distribution associated with typhoon Matsa (2005). Chin Sci Bull, 2007, 52: 972–983CrossRefGoogle Scholar
  31. 31.
    Gao S T, Tao S Y, Ding Y H. Interaction between upper air waves and East Asia jet during cold wave (in Chinese). Chin J Atmos Sci, 1992, 16: 718–724Google Scholar
  32. 32.
    Cochran W G, Bliss C I. Discriminant function with covariance. Ann Math Statist, 1948, 19: 151–176CrossRefGoogle Scholar
  33. 33.
    Miller R G. Statistical prediction by discriminant analysis. Meteorol Monmg, 1962, 4: 1–15Google Scholar
  34. 34.
    Suzuki E. Categorical prediction schemes of rainfall types by discriminant analysis. Papers Meteorol Geophys, 1964, 15: 119–160Google Scholar
  35. 35.
    Wang J Z, Yang Y Q. Modern Weather Engineering (in Chinese). Beijing: China Meteorological Press, 2000. 334–339Google Scholar
  36. 36.
    Keenan T D. Forecasting tropical cyclone motion using a discriminant analysis procedure. Mon Rev, 1986, 144: 434–441CrossRefGoogle Scholar
  37. 37.
    Yang Y Q, Hou Q, Wang J Z. A study on prediction of SDS annual, tendency and operational testing. In: Fifth International Workshop on Sand and Dust Storms (SDS). Beijing: Meteorology Press, 2008. 21–24Google Scholar
  38. 38.
    Yang Y Q, Wang J Z. An integrated decision method for prediction of tropical cyclone movement by using genetic algorithm. Sci China Ser D-Earth Sci, 2005, 48: 429–440CrossRefGoogle Scholar
  39. 39.
    Li Q L, Xu X F. A self-adaptive genetic algorithm for partner selection of agile virtual enterprise (in Chinese). Infor Adv Techn, 2001, 10: 66–69Google Scholar

Copyright information

© Science China Press and Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • YuanQin Yang
    • 1
  • JiZhi Wang
    • 1
  • Qing Hou
    • 1
  • Yi Li
    • 1
  • ChunHong Zhou
    • 1
  1. 1.Key Laboratory for Atmospheric Chemistry, Center for Atmospheric Watch and ServicesChinese Academy of Meteorological Sciences, China Meteorological AdministrationBeijingChina

Personalised recommendations